{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reciprocal transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for Q-Q plots\n",
    "import scipy.stats as stats\n",
    "\n",
    "# the dataset for the demo\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 20640\n",
      "\n",
      ":Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      ":Attribute Information:\n",
      "    - MedInc        median income in block group\n",
      "    - HouseAge      median house age in block group\n",
      "    - AveRooms      average number of rooms per household\n",
      "    - AveBedrms     average number of bedrooms per household\n",
      "    - Population    block group population\n",
      "    - AveOccup      average number of household members\n",
      "    - Latitude      block group latitude\n",
      "    - Longitude     block group longitude\n",
      "\n",
      ":Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
      "\n",
      "The target variable is the median house value for California districts,\n",
      "expressed in hundreds of thousands of dollars ($100,000).\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "A household is a group of people residing within a home. Since the average\n",
      "number of rooms and bedrooms in this dataset are provided per household, these\n",
      "columns may take surprisingly large values for block groups with few households\n",
      "and many empty houses, such as vacation resorts.\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = fetch_california_housing()\n",
    "\n",
    "print(data.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the California House price data from Scikit-learn\n",
    "X, y = fetch_california_housing(return_X_y=True, as_frame=True)\n",
    "\n",
    "# display top 5 rows\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot histogram and Q-Q plots to explore the variable distribution\n",
    "\n",
    "\n",
    "def diagnostic_plots(df, variable):\n",
    "\n",
    "    # function to plot a histogram and a Q-Q plot\n",
    "    # side by side, for a certain variable\n",
    "\n",
    "    plt.figure(figsize=(15, 6))\n",
    "\n",
    "    # histogram\n",
    "    plt.subplot(1, 2, 1)\n",
    "    df[variable].hist(bins=30)\n",
    "    plt.title(f\"Histogram of {variable}\")\n",
    "\n",
    "    # q-q plot\n",
    "    plt.subplot(1, 2, 2)\n",
    "    stats.probplot(df[variable], dist=\"norm\", plot=plt)\n",
    "    plt.title(f\"Q-Q plot of {variable}\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check function output\n",
    "\n",
    "# AveOccup = average number of household members (number of people per household)\n",
    "# AveOccup = number of members / number of houses\n",
    "\n",
    "diagnostic_plots(X, \"AveOccup\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reciprocal transformation with NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the dataframe where we will store the modified\n",
    "# variables\n",
    "\n",
    "X_tf = X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the reciprocal to a set of variables\n",
    "\n",
    "X_tf[\"AveOccup\"] = np.reciprocal(X_tf[\"AveOccup\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use diagnostic plot function to corroborate variable transformation\n",
    "\n",
    "# AveOccup = number of households per number of people)\n",
    "# AveOccup = number of houses / number of people\n",
    "\n",
    "diagnostic_plots(X_tf, \"AveOccup\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reciprocal transformation with Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import FunctionTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the dataframe where we will store the modified\n",
    "# variables\n",
    "\n",
    "X_tf = X.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start the transformer with NumPy reciprocal as parameter\n",
    "\n",
    "transformer = FunctionTransformer(func=np.reciprocal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform subset of dataframe\n",
    "\n",
    "X_tf[\"AveOccup\"] = transformer.transform(X[\"AveOccup\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use diagnostic plot function to corroborate variable transformation\n",
    "\n",
    "diagnostic_plots(X_tf, \"AveOccup\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reciprocal transformation with Feature-engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_engine.transformation import ReciprocalTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ReciprocalTransformer(variables=&#x27;AveOccup&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ReciprocalTransformer<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ReciprocalTransformer(variables=&#x27;AveOccup&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "ReciprocalTransformer(variables='AveOccup')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize the transformer with the varible that\n",
    "# we want to modify\n",
    "\n",
    "rt = ReciprocalTransformer(variables=\"AveOccup\")\n",
    "\n",
    "# fit transformer to the entire dataframe\n",
    "rt.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the selected variable in our data set\n",
    "\n",
    "X_tf = rt.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use diagnostic plot function to corroborate variable transformation\n",
    "\n",
    "diagnostic_plots(X_tf, \"AveOccup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_tf = rt.inverse_transform(X)\n",
    "\n",
    "# use diagnostic plot function to corroborate variable transformation\n",
    "\n",
    "diagnostic_plots(X_tf, \"AveOccup\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "237.467px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
